Search Results (196)

An integrated field–analytical framework was applied to quantify the impact of two small-settlement treatment facilities (TF1 and TF2) on the Irtysh River (East Kazakhstan). The main objective of this study is to quantify effluent-driven dilution and non-conservative changes in key water-quality indicators downstream of TF1 and TF2 and to evaluate parsimonious models for predicting effluent-outlet BOD and COD from upstream measurements. Paired upstream–downstream control sections are sampled in 2024–2025 for 22 indicators, and plant influent–effluent records are compiled for key wastewater variables. Chloride-based conservative mixing indicated very strong dilution (approximately $D\approx 2.0\times {10}^3$ for TF1 and $D\approx 4.2\times {10}^2$ for TF2). Deviations from the mixing line were summarized using a transformation diagnostic $\theta$. At TF1, several constituents exceeded mixing expectations ($\theta \approx 13$ for COD, $\theta \approx 42$ for ammonium, and $\theta \approx 6$ for phosphates), while nitrate shows net attenuation $\theta <0$. At TF2, $\theta$ values cluster near unity, indicating modest deviations. Under a small-sample regime $\left(\mathrm{N}=10\right)$ and leave-one-out validation, regularized regression provided accurate forecasts of effluent-outlet BOD and COD. Lasso under LOOCV performed best (BOD_after: RMSE = 0.626, MAE = 0.459, and $R^2=0.976$; COD_after: RMSE = 0.795, MAE = 0.634, and $R^2=0.997$). The results reconcile strong reach-scale dilution with constituent-specific local departures and support targeted modernization and operational forecasting for water-quality management in small facilities. Full article

The growing threat of organic pollution to surface waters necessitates the development of rapid and scalable monitoring tools that transcend the limitations of the standard 5-day biochemical oxygen demand (BOD₅) test. This study presents a novel approach by developing a highly stable and rapid BOD biosensor based on the microorganism Paracoccus yeei, immobilized within a sol–gel-derived xerogel matrix synthesized on a polyhydroxybutyrate (PHB) substrate. The PHB-supported xerogel significantly enhanced microbial viability and sensor stability. This biosensor demonstrated a correlation (R² = 0.93) with the standard BOD₅ method across 53 diverse water samples from the Tula region, Russia, providing precise results in just 5 min. The second pillar of our methodology involved analyzing multi-year Landsat satellite imagery via the Global Surface Water Explorer to map hydrological changes and identify zones of potential anthropogenic impact. The synergy of rapid ground-truth biosensor measurements and remote sensing analysis enabled a comprehensive spatial assessment of water quality, successfully identifying and ranking pollution sources, with wastewater discharges and agro-industrial facilities constituting the most significant factors. This work underscores the high potential of PHB–xerogel composites as efficient immobilization matrices and establishes a powerful, scalable framework for regional environmental monitoring by integrating advanced biosensor technology with satellite observation. Full article

Swine effluents require effective treatment due to their high pollutant load, particularly nitrogen and phosphorus, which can cause eutrophication of water bodies. This study focused on monitoring nutrient removal in a sequential biological reactor through online measurements of parameters such as dissolved oxygen (DO), pH, oxidation-reduction potential (ORP), and total alkalinity during the treatment of effluents from a pig slaughterhouse. A laboratory-scale reactor was used, operated with timer switches in an anaerobic–aerobic–anoxic sequence, a sludge retention time (SRT) of 25 days, and an operational cycle time of 16 h. The reactor demonstrated notable efficiency in contaminant removal, with an average organic matter removal of 87.1% measured as chemical oxygen demand (COD) and 95.5% as biochemical oxygen demand (BOD). Regarding nitrogen and phosphorus removal, a 69.4% reduction in total nitrogen (TN) and a 53.2% reduction in total phosphorus (TP) were observed. The pH, ORP, and DO profiles showed a clear correlation with the nutrient removal processes, allowing optimization of the phase durations in the reactor to enhance treatment efficiency. Full article

Water is a vital resource for sustaining life; however, it is increasingly at risk due to escalating demand and heightened pollution levels. Swimming pool facilities generate diverse wastewater streams whose management offers opportunities for water recovery within a circular economy framework. The quantitative and qualitative analysis of research identifies five primary categories of wastewater: swimming pool basin outflow, filter washings, rainwater and meltwater, sanitary wastewater, and technological sludge, at a public swimming pool complex in Poland. Annual volumes were determined through direct measurements and calculations: pool basin outflow—2829.7 m³/year; filter washings—7179.2 m³/year; rainwater and meltwater—1172.6 m³/year; sanitary wastewater—5849.3 m³/year; and technological sludge—90.1 m³/year. Laboratory testing included physicochemical parameters (pH, redox potential, conductivity, COD, BOD, nutrients, heavy metals) and microbiological parameters (Escherichia coli, Pseudomonas aeruginosa, Legionella spp., Salmonella spp., Ascaris sp., Trichuris sp., Toxocara sp., Coagulase-positive Staphylococcus). The results showed that the filter washings, despite exceeding the limits for total suspended solids and combined chlorine, exhibited stable quality and significant volume, making them the most promising candidate for reuse after treatment. Rainwater quality was compromised by elevated heavy metal concentrations (Zn: 244.67 mg/L, Pb: 92.33 mg/L), while technological sludge exceeded the legal pollutant thresholds, classifying it as hazardous waste. The experimental conditions included year-round monitoring of operational flows, standardised backwash cycles every three days, and sampling under routine operational load. The findings support the development of targeted treatment systems that allow the recirculation of up to 7000 m³/year of water, thus reducing the demand for potable water and operational costs in swimming pool facilities. Full article

This paper aims to assess the water quality of the Ismailia Canal, Egypt, in accordance with Article 49 of Law 92/2013. QUAL2K and Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) are utilized to simulate the water quality parameters of dissolved oxygen (DO), pH, biological oxygen demand (BOD), chemical oxygen demand (COD), total phosphorus (TP), nitrate nitrogen (NO₃-N), and ammonium (NH₃-N) in winter and summer 2023. The parameters of the QUAL2K and CNN-LSTM models were calibrated and validated in both winter and summer through trial and error, until the simulated results agreed well with the observed data. Additionally, the model’s performance was measured using different statistical criteria such as mean absolute error (MAE), root mean square (RMS), and relative error (RE). The results showed that the simulated values were in good agreement with the observed values. The results show that all parameter concentrations follow and did not exceed the limit of Article 49 of Law 92/2013 in winter and summer, except for dissolved oxygen concentration (8.73–4.53 mg/L) in winter and summer, respectively, which exceeds the limit of 6 mg/L, and in June, biological oxygen demand exceeds the limit of 6 mg/L due to increased organic matter. It is imperative to compare QUAL2K and CNN-LSTM models because QUAL2K provides a physics-based simulation of water quality processes, whereas CNN-LSTM employs deep learning in modeling complex temporal patterns. The two models enhance prediction accuracy and credibility towards enabling enhanced decision-making for Ismailia Canal water management. This research can be part of a decision support system regarding maximizing the benefits of the Ismailia Canal. Full article

This paper presents the results of a survey of the designed biological wastewater treatment facilities of an enterprise specializing in the production of rubber products. The aim of the study was to assess the efficiency of wastewater treatment systems under the conditions of a salvo discharge of industrial effluents that differ in composition from domestic wastewater. The analysis covered the parameters of water supply, water disposal, and wastewater characteristics at various stages of treatment. Three samples were taken: after washing the premises (WW1), at the inlet to the treatment facility (WW2), and at the outlet after treatment (WW3). Experimental assessment of the purification efficiency for key pollutants showed a high degree of removal of surfactants (91.2%), oil products (84.4%), and COD (63.4%). However, phosphorus–phosphate turned out to be significantly higher than the norm—2.32 mg/L with an acceptable level of 0.2 mg/L—which corresponds to an excess of 11.6 times. A low degree of ammonium nitrogen removal was also revealed—62%. Calculations showed a critically high ratio of COD/BOD5 = 5.1 with the recommended <2.6, which indicates a small share of biodegradable substances and the need to implement physical and chemical treatment methods. The absence of the characteristic smell of household wastewater and the presence of black inorganic sediment confirm the toxicity of emerging pollutants for activated sludge. It is concluded that the installed biological treatment system cannot cope with the salvo loads of industrial wastewater. Optimization measures are proposed: preliminary local treatment, dosed feed, and a separate treatment system. Full article

Urban and peri-urban lakes are increasingly threatened by water quality degradation due to rising anthropogenic pressures and environmental variability. This study proposes an integrated framework that combines multi-source data and machine learning to estimate and monitor three key water quality parameters: turbidity, total dissolved solids (TDS), and biological oxygen demand (BOD). Field measurements from three lakes in West Bengal, India, Rabindra Sarovar, Mirikh Lake, and Hanuman Ghat Lake, were combined with Landsat-8 satellite imagery, meteorological data, and land use information. Three modeling scenarios were developed: (i) using only remote sensing indices, (ii) combining remote sensing indices with meteorological variables, and (iii) integrating remote sensing indices, meteorological data, and land use features. Principal component analysis (PCA) was used to reduce dimensionality and redundancy. Machine learning models, namely, XGBoost, Decision Tree, and Ridge Regression, were trained and evaluated using R² and RMSE (Root Mean Square Error) metrics. The third scenario outperformed the others, with Ridge Regression achieving the highest accuracy for BOD prediction (R² = 0.99). Spatiotemporal patterns revealed persistently high BOD levels along urban lake fringes and post-monsoon spikes in turbidity and TDS, especially in agriculturally influenced zones. These patterns were closely linked to land use practices, rainfall-driven runoff, and point-source pollution. This study underscores the effectiveness of remote sensing and machine learning as scalable tools for real-time water quality monitoring, promoting sustainability through informed lake management strategies in India. Full article

Fresh water is vital for human activities; however, an increase in the contamination of water bodies has been observed, so it is necessary to monitor the degree of contamination and take measures to preserve it. In Altamira, Tamaulipas, the Guayalejo-Tamesí River basin has three estuaries and seven lagoons, including Laguna El Conejo, of which the National Water Commission only monitors one. The objective of this research is to determine water quality on the basis of the parameters COD, BOD₅, T, pH, and sediment characteristics. The open reflux method was used according to NMX-AA-030-SCFI-2012 for COD, BOD Track II, HACH equipment for BOD₅, and the granulometric characterization recommended by the Unified Soil Classification System ASTM D-2487-17. The water was found to be uniformly contaminated throughout its length in the range of 117.3–200 mg/L COD, and BOD₅ ranged from 15.8–112.75 mg/L throughout the study period, with sediments dominated by poorly graded soil and fine clay. Comprehensive management is needed because the BOD₅/COD ratio varies between 0.11and 0.56, indicating that it contains recalcitrant organic matter, which is difficult to biodegrade. Full article

Substantial boosts in the low-energy nano-oxygenation of incoming process water were achieved at a municipal wastewater treatment plant (WWTP) upstream of activated sludge (AS) aeration lanes on a single-pass basis by means of an electric field nanobubble (NB) generation method (with unit residence times of the order of just 10–15 s). Both ambient air and O₂ cylinders were used as gas sources. In both cases, it was found that the levels of dissolved oxygen (DO) were maintained far higher for much longer than those of conventionally aerated water in the AS lane—and at DO levels in the optimal operational WWTP oxygenation zone of about 2.5–3.5 mg/L. In the AS lanes themselves, there were also excellent conversions to nitrate from nitrite, owing to reactive oxygen species (ROS) and some improvements in BOD and E. coli profiles. Nanobubble-enhanced Dissolved Air Flotation (DAF) was found to be enhanced at shorter times for batch processes: settlement dynamics were slowed slightly initially upon contact with virgin NBs, although the overall time was not particularly affected, owing to faster settlement once the recruitment of micro-particulates took place around the NBs—actually making density-filtering ultimately more facile. The development of machine learning (ML) models predictive of NB populations was carried out in laboratory work with deionised water, in addition to WWTP influent water for a second class of field-oriented ML models based on a more narrow set of more easily and quickly measured data variables in the field, and correlations were found for a more facile prediction of important parameters, such as the NB generation rate and the particular dependent variable that is required to be correlated with the efficient and effective functioning of the nanobubble generator (NBG) for the task at hand—e.g., boosting dissolved oxygen (DO) or shifting Oxidative Reductive Potential (ORP). Full article

The pollution of water bodies has deteriorated the quality of freshwater and the health of the natural ecosystem. In the present study, the water quality index (WQI) was used to evaluate the spatial and temporal contamination levels in Lake Yuriria, Guanajuato, Mexico. Water quality was monitored at 27 different locations (monitoring points) in the dry season (April) and after the rainy season (November), measuring 21 physicochemical water parameters, 2 biological parameters, and 19 metal concentrations. The data analysis revealed that Yuriria Lake is a eutrophic water body. Six monitoring points exhibited a poor WQI (25–50) in April, and seven monitoring sites were classified as having poor water quality in November. The remaining monitoring points showed a WQI categorized as fair (51–70) in both periods. The present study analyzes an extensive distribution of monitoring points over the lake’s surface in two periods, showing a significant spatial and temporal representation of water quality. In addition, the major pollution sources identified include agricultural runoff and effluents from a nearby waterway and freshwater river. Finally, the key physicochemical parameters that determined the water quality were identified. BOD₅, NH₄⁺, P, orthophosphates, DO, conductivity, TSS, and color were linked to anthropogenic pollution sources, and Li, Ni, Zn, Cd, Ba, and Pb concentrations were linked to natural contamination sources. This study demonstrates the utility and versatility of these methodologies in water quality research, and it is the first spatial and temporal WQI analysis of Yuriria Lake. Full article

Polyols derived from poly(vinyl alcohol) (PVA) have not been reported before. The hydroxyalkylation of PVA with oxiranes leads to powdered or gum-like products that are not miscible with isocyanates and therefore useless as sources of polyurethane foams. Glycidol and ethylene carbonates were used to dissolve and convert PVA into liquid polyol. The physical properties of the PVA-derived polyol, such as the density, viscosity, and surface tension, were determined. The polyol was then used to obtain rigid polyurethane foams (PUFs). Foaming conditions were optimized, and the apparent density, volume water uptake, dimensional stability, heat conductance coefficient, pore size, thermal resistance, compressive strength, and glass transition temperature of the obtained PUFs were determined. The properties of the obtained PUFs were similar to those of classic rigid PUFs, but the thermal resistance of the former is better. Specifically, PVA-derived PUFs are thermally resistant at temperatures of up to 150 °C. Furthermore, they are ecologically safe; in standard soil conditions, 54.6% or 100% biodegradation of the foams in cube and powder form, respectively, was observed, as measured by BOD after 28 days of storage. Full article

The Mekong Delta, an important agricultural and economic hub in Vietnam, has suffered from severe water quality issues caused by both natural and anthropogenic forces. This paper aims to conduct a rational statistical approach to evaluate the current situation of surface water quality in the Mekong Delta, applying Factor Analysis (FA), Principal Component Analysis (PCA), and Agglomerative Hierarchical Clustering (AHC) to a database of 3117 samples collected by national and provincial monitoring stations. The results revealed significant contamination with organic pollutants (BOD5: 3.50–172.870 mg/L, COD: 6.493–472.984 mg/L), pesticides (e.g., DDTs: n.d to 1.227 mg/L), trace metals (As: 0.006–0.046 mg/L, Cr: n.d–1.960 mg/L), and microbial indicators (Coliforms: n.d–45,100 MPN/100 mL), often higher than the WHO drinking water threshold. PCA/AHC analysis identified the following five major pollution components: (1) organic pollution and sewage/industrial and deposited chemicals (PCA1—23.08% variance); (2) pesticide and agricultural runoff derived contamination with Hg (PCA2—15.44%); (3) microbial pollution of the water was found to correlate positively with Zn and Cu content (PCA3—8.90%); (4) salinity was found to mobilize As and Cr (PCA4—8.00%); (5) nutrient/microbial pollution presumably from agricultural and sewage inputs (PCA5—7.22%). AHC showed some spatial variability that grouped samples in urban/industrial (Cluster 1), rural/agricultural (Cluster 2), and a highly contaminated one, where water was toxic and presented with microbial and Cd contamination (Cluster 3). Levels of pesticides, Cr, and microbial pollution were higher than reported in previous Mekong Delta studies and exceeded regional trends. These results emphasize the importance of holistic water management strategies, including better wastewater treatment, pesticide control, sustainable farming, and climate-adaptive measures to reduce saltwater intrusion and safeguard drinking water quality for the Mekong Delta. Full article

One of the most important factors affecting water resources is livestock development. This study focuses on estimating the water demands of beef and dairy cattle breeding, as well as the corresponding products, in the Regional Unit of Karditsa (Greece), while simultaneously assessing the pollution caused by this activity in water bodies. The impacts are measured using the water footprint (WF) approach across its three dimensions (green, blue, and gray), considering the quantity of feed and water utilized by each animal type and the production system applied in the research area. For beef production, the intensive system shows a total WF of 90,535 m³/ton (gray 88%, green 9%, blue 3%), while the semi-intensive system totals 82,027 m³/ton (gray 84%, green 12%, blue 4%). For dairy cows, the total WF reaches 2750 m³/year/ton of milk (gray 81%, green 14%, blue 5%). Gray WF was estimated based on pollutant loads from livestock waste using concentration thresholds for biochemical oxygen demand (BOD₅), nitrogen (N), and phosphorus (P), providing a clearer view of water quality degradation linked to livestock activities. These findings can guide regional directorates in addressing key water-related pressures from livestock production. Full article

Modular wastewater treatment units in large residential complexes in India’s crowded cities often lack stringent monitoring due to cost constraints and limited technical manpower. Although these plants must meet effluent standards, testing often requires sending samples to external labs, causing delays and added costs. As a result, they are rarely monitored, risking improper effluent discharge. Quick, cost-effective assessments of effluent quality could significantly improve plant operation and maintenance. Addressing the special challenges faced by such wastewater treatment systems, artificial intelligence (AI)-based soft sensors and virtual instruments have been developed to forecast effluent quality with the help of a water quality parameter that is inexpensively, easily, and immediately measurable with a hand-held device. In this study, advanced artificial neural network (ANN)-based soft sensors were developed to enhance the monitoring and management of effluent quality in five modular wastewater treatment plants in Bangalore. The models serve as virtual instruments for the measurement of total suspended solids (TSS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), using the wastewater turbidity as the input parameter. By using these AI models, operators can better anticipate and manage water quality, ultimately contributing to more efficient and effective wastewater treatment operations. This innovative approach represents a significant advancement in wastewater treatment technology providing a practical and efficient solution to streamline monitoring and enhance overall plant performance. Full article

The constant increase in industrialization and urbanization has led to the regular discharge of wastewater into the environment in excessive amounts, which has caused significant impacts on both human and wildlife ecosystems. The sustainable management and treatment of wastewater, whether of industrial or domestic origin, represents a crucial challenge in this century. In this study, phytoremediation was employed as a wastewater treatment strategy using two species of aquatic macrophytes: water hyacinth (Eichhornia crassipes) and duckweed (Lemna minor). The study was conducted over seven consecutive evaluation periods, with five-day intervals between each. The objective was to apply the multivariate HJ-Biplot methodology to evaluate the effects of phytoremediation of two species of aquatic microphytes on the physicochemical characteristics of wastewater from Milagro canton, Ecuador. Additionally, a microbiological analysis was conducted to determine the effectiveness of the floating macrophytes. The analysis was based on the measurement of various physicochemical parameters, such as pH, electrical conductivity (EC), dissolved oxygen (DO), oxidation–reduction potential (ORP), salinity, total dissolved solids (TDSs), biochemical oxygen demand (BOD), chemical oxygen demand (COD), hardness, and temperature. The results showed that the highest efficiency in pollutant removal was achieved with duckweed (Lemna minor) in five out of nine measured parameters, suggesting that this species was the most effective compared to the control sample and Eichhornia crassipes. The capacity of these macrophytes for wastewater treatment was confirmed by this study. To ensure effective water purification, timely extraction of aquatic macrophytes from water bodies is recommended. If this collection is not properly carried out, the nutrients absorbed and stored in the plant tissues may be released back into the aquatic environment due to plant decomposition. Full article